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comparison deep/stacked_dae/old/mnist_sda.py @ 275:7b4507295eba

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author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Mon, 22 Mar 2010 10:20:10 -0400
parents c8fe09a65039
children
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274:44409b6652aa 275:7b4507295eba
1 #!/usr/bin/python
2 # coding: utf-8
3
4 # TODO: This probably doesn't work anymore, adapt to new code in sgd_opt
5 # Parameterize call to sgd_optimization for MNIST
6
7 import numpy
8 import theano
9 import time
10 import theano.tensor as T
11 from theano.tensor.shared_randomstreams import RandomStreams
12
13 from sgd_optimization import SdaSgdOptimizer
14 import cPickle, gzip
15 from jobman import DD
16
17 MNIST_LOCATION = '/u/savardf/datasets/mnist.pkl.gz'
18
19 def sgd_optimization_mnist(learning_rate=0.1, pretraining_epochs = 2, \
20 pretrain_lr = 0.1, training_epochs = 5, \
21 dataset='mnist.pkl.gz'):
22 # Load the dataset
23 f = gzip.open(dataset,'rb')
24 # this gives us train, valid, test (each with .x, .y)
25 dataset = cPickle.load(f)
26 f.close()
27
28 n_ins = 28*28
29 n_outs = 10
30
31 hyperparameters = DD({'finetuning_lr':learning_rate,
32 'pretraining_lr':pretrain_lr,
33 'pretraining_epochs_per_layer':pretraining_epochs,
34 'max_finetuning_epochs':training_epochs,
35 'hidden_layers_sizes':[100],
36 'corruption_levels':[0.2],
37 'minibatch_size':20})
38
39 optimizer = SdaSgdOptimizer(dataset, hyperparameters, n_ins, n_outs)
40 optimizer.pretrain()
41 optimizer.finetune()
42
43 if __name__ == '__main__':
44 sgd_optimization_mnist(dataset=MNIST_LOCATION)
45